Developing a 3D Constrained Variational Analysis Method to Calculate Large Scale Forcing Data and the Applications

Thursday, 18 December 2014
Shuaiqi Tang, Stony Brook University, Stony Brook, NY, United States and Ming-Hua Zhang, SUNY Stony Brook, Stony Brook, NY, United States
Large-scale forcing data (vertical velocities and advective tendencies) are important atmospheric fields to drive single-column models (SCM), cloud-resolving models (CRM) and large-eddy simulations (LES), but they are difficult to calculate accurately. The current 1-dimensional constrained variational analysis (1D CVA) method (Zhang and Lin, 1997) used by the Atmospheric Radiation Measurement (ARM) program is limited to represent the average of a sounding network domain. We extended the original 1D CVA algorithm into 3-dimensional along with other improvements, calculated gridded large-scale forcing data, apparent heating sources (Q1) and moisture sinks (Q2), and compared with 5 reanalyses: ERA-Interim, NCEP CFSR, MERRA, JRA55 and NARR for a mid-latitude spring cyclone case. The results from a case study for in March 3rd 2000 at the Southern Great Plain (SGP) show that reanalyses generally captured the structure of the mid-latitude cyclone, but they have serious biases in the 2nd order derivative terms (divergences and horizontal derivations) at regional scales of less than a few hundred kilometers. Our algorithm provides a set of atmospheric fields consistent with the observed constraint variables at the surface and top of the atmosphere better than reanalyses. The analyzed atmospheric fields can be used in SCM, CRM and LES to provide 3-dimensional dynamical forcing, or be used to evaluate reanalyses or model simulations.